| IHME | WHO | Dif | % difference (WHO / IHME) | % difference (IHME / WHO) | ||
|---|---|---|---|---|---|---|
| Total | HIV+TB only | 211604 | 389042 | 177438.13321 | 83.85% | -45.61% |
| TB only | 1111312 | 1379440 | 268128.44955 | 24.13% | -19.44% | |
| Total TB | 1322916 | 1768482 | 445566.58275 | 33.68% | -25.19% | |
| Adults | HIV+TB only | 177567 | 348026 | 170458.90473 | 96% | -48.98% |
| TB only | 1075691 | 1210620 | 134929.12946 | 12.54% | -11.15% | |
| Total TB | 1253257 | 1558645 | 305388.03419 | 24.37% | -19.59% | |
| Children | HIV+TB only | 34037 | 41016 | 6979.22848 | 20.5% | -17.02% |
| TB only | 35621 | 168821 | 133199.32009 | 373.93% | -78.9% | |
| Total TB | 69659 | 209837 | 140178.54857 | 201.24% | -66.8% | |
| Female | HIV+TB only | 78110 | 143496 | 65386.51804 | 83.71% | -45.57% |
| TB only | 367764 | 352488 | 15276.43876 | -4.15% | 4.33% | |
| Total TB | 445874 | 495984 | 50110.07929 | 11.24% | -10.1% | |
| Male | HIV+TB only | 99457 | 204471 | 105013.90757 | 105.59% | -51.36% |
| TB only | 707927 | 858132 | 150205.56821 | 21.22% | -17.5% | |
| Total TB | 807383 | 1062603 | 255219.47578 | 31.61% | -24.02% | |
| AMR | HIV+TB only | 579 | 620 | 41.31917 | 7.14% | -6.66% |
| TB only | 2036 | 1914 | 122.37010 | -6.01% | 6.39% | |
| Total TB | 2615 | 2534 | 81.05093 | -3.1% | 3.2% | |
| EMR | HIV+TB only | 165 | 533 | 368.30203 | 223.05% | -69.04% |
| TB only | 14658 | 14572 | 85.51575 | -0.58% | 0.59% | |
| Total TB | 14823 | 15106 | 282.78629 | 1.91% | -1.87% | |
| EUR | HIV+TB only | 212 | 374 | 161.68749 | 76.15% | -43.23% |
| TB only | 2383 | 2999 | 615.93832 | 25.85% | -20.54% | |
| Total TB | 2595 | 3373 | 777.62581 | 29.97% | -23.06% | |
| SEA | HIV+TB only | 19310 | 28870 | 9560.04060 | 49.51% | -33.11% |
| TB only | 333250 | 345889 | 12639.43214 | 3.79% | -3.65% | |
| Total TB | 352560 | 374759 | 22199.47275 | 6.3% | -5.92% | |
| WPR | HIV+TB only | 2057 | 2010 | 47.13348 | -2.29% | 2.35% |
| TB only | 39055 | 28351 | 10704.20283 | -27.41% | 37.76% | |
| Total TB | 41112 | 30361 | 10751.33632 | -26.15% | 35.41% |
Table with model output for estimating likelihood or magnitude of difference in estimates by HIV, age, sex, and region.
This section is unfinished.
Rankings of highest absolute and standardized differences for IHME and WHO.
Rankings of highest absolute and standardized differences for IHME and WHO.
The below scatterplot shows the correlation between WHO (x-axis) estimates and IHME (y-axis) estimates, with each point colored by its (WHO-defined) region.
In the following four charts, Libya has been excluded as an outlier.
# A tibble: 2 x 2
prevsurvey median_adjusted_stand_dif
<dbl> <dbl>
1 0 - 5.20
2 1.00 16.5
Error: <text>:16:0: unexpected end of input
14: # Those countries with low case detection (oftentimes have prev survey),
15: # WHO estimates more deaths than IHME
^
Linear regression to estimate effect of prevalence survey on absolute difference in cases (WHO minus IHME), adjusting for region.
95% confidence intervals
Linear regression to estimate effect of prevalence survey on adjusted standardized difference in cases, adjusting for region.
95% confidence intervals
(Unfinished)
Correlation of adjusted stand diff with a) HIV prevalence, CDR by both, CFR, MDR prevalence.
cor(df$adjusted_stand_dif, df$newrel_hivpos, use = 'complete.obs')
[1] 0.09204849
cor(df$adjusted_stand_dif, df$gb_c_cdr, use = 'complete.obs')
[1] -0.3688292
cor(df$adjusted_stand_dif, df$cdr_ihme, use = 'complete.obs')
[1] 0.4355758
cor(df$adjusted_stand_dif, df$case_fatality_rate_2014, use = 'complete.obs')
[1] -0.1353054
cor(df$adjusted_stand_dif, df$case_fatality_rate_2012_to_2014, use = 'complete.obs')
[1] -0.1171681
cor(df$adjusted_stand_dif, df$case_fatality_rate_2015, use = 'complete.obs')
[1] -0.1586533
cor(df$adjusted_stand_dif, df$case_fatality_rate_2014_new, use = 'complete.obs')
[1] -0.1587409
cor(df$adjusted_stand_dif, df$case_fatality_rate_2012_to_2014_new, use = 'complete.obs')
[1] -0.1147299
cor(df$adjusted_stand_dif, df$case_fatality_rate_2015_new, use = 'complete.obs')
[1] -0.1760415
cor(df$adjusted_stand_dif, df$case_fatality_rate_2015_adjusted, use = 'complete.obs')
[1] -0.1825428
cor(df$adjusted_stand_dif, df$p_mdr_new, use = 'complete.obs')
[1] 0.05559062
cor(df$adjusted_stand_dif, df$reported_mdr, use = 'complete.obs')
[1] -0.0131539
Does region affect likelihood of having a prevalence survey?
xt <- table(df$prevsurvey, df$who_region)
xt
AFR AMR EMR EUR SEA WPR
0 37 37 20 52 8 22
1 10 0 2 0 3 4
chisq.test(xt)
Pearson's Chi-squared test
data: xt
X-squared = 21.511, df = 5, p-value = 0.0006482
Does having a prev survey affect the adjusted stand diff?
t.test(x = df$adjusted_stand_dif[df$prevsurvey == 0],
y = df$adjusted_stand_dif[df$prevsurvey == 1])
Welch Two Sample t-test
data: df$adjusted_stand_dif[df$prevsurvey == 0] and df$adjusted_stand_dif[df$prevsurvey == 1]
t = -2.1643, df = 21.066, p-value = 0.04207
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-39.2150725 -0.7865973
sample estimates:
mean of x mean of y
-4.545393 15.455442
April 18, 2018
Alberto Method: top and bottom 10 stand diff (children only)
Martien Method: top and bottom 10 stand diff (children only)
Concordance between adult and child measures (Alberto method)
Concordance between adult and child measures (Martien method)
Alberto Method: top and bottom 10 stand diff (HIV only, all ages)
Martien Method: top and bottom 10 stand diff (HIV, all ages)
Is CDR associated with stand diff in the linear regression among those countries without prevalence survey?
x <- df
x$gb_c_cdr[!is.finite(x$gb_c_cdr)] <- NA
x$original_stand_dif[!is.finite(x$original_stand_dif)] <- NA
fit <- lm(gb_c_cdr ~ original_stand_dif, data = x[x$prevsurvey == 0,])
summary(fit)
Call:
lm(formula = gb_c_cdr ~ original_stand_dif, data = x[x$prevsurvey ==
0, ])
Residuals:
Min 1Q Median 3Q Max
-42.50 -12.33 4.16 10.80 23.21
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 76.0320 1.1661 65.201 < 0.0000000000000002 ***
original_stand_dif -0.3555 0.1123 -3.166 0.00187 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 14.35 on 151 degrees of freedom
(23 observations deleted due to missingness)
Multiple R-squared: 0.06226, Adjusted R-squared: 0.05605
F-statistic: 10.03 on 1 and 151 DF, p-value: 0.001868
Yes. Among countries without a prevalence survey, a 1 unit increase in the standardized difference is associated with a 0.355 unit decrease in the CDR. This is significant (p < 0.01).
Afternoon of Wednesday, April 19. All charts are WHO minus IHME
| Country | Value |
|---|---|
| India | 49508.8443 |
| Nigeria | 32003.9550 |
| Indonesia | 12752.4252 |
| Bangladesh | 8616.7562 |
| Tanzania | 5992.6238 |
| Dem. Rep. Congo | 5147.3671 |
| China | 3214.4091 |
| Myanmar | 3169.4789 |
| Pakistan | 2334.5255 |
| Angola | 2089.3906 |
| Zimbabwe | -2191.6025 |
| South Africa | -1524.9994 |
| Malawi | -1022.3595 |
| Uganda | -504.5276 |
| Burkina Faso | -499.9316 |
| Lesotho | -415.1728 |
| Rwanda | -378.9136 |
| Burundi | -279.2630 |
| Swaziland | -223.2993 |
| Namibia | -170.1219 |
| Country | Value |
|---|---|
| North Korea | 100.00000 |
| Bangladesh | 99.41480 |
| Timor-Leste | 99.01265 |
| Papua New Guinea | 98.80690 |
| Libya | 97.09310 |
| Viet Nam | 97.08583 |
| Thailand | 95.00197 |
| United Arab Emirates | 94.13214 |
| Haiti | 93.15330 |
| Myanmar | 92.41885 |
| Rwanda | -68.52748 |
| Zimbabwe | -55.26266 |
| Azerbaijan | -53.23740 |
| Burkina Faso | -49.28143 |
| Japan | -47.64091 |
| Swaziland | -40.43862 |
| Honduras | -36.12995 |
| Namibia | -33.76727 |
| Malawi | -30.59634 |
| Eritrea | -30.44260 |
Downloadable table with full data
| Country | Value |
|---|---|
| Nigeria | 184617.353 |
| Bangladesh | 41246.468 |
| Tanzania | 32279.694 |
| South Africa | 30633.208 |
| Mozambique | 27458.985 |
| Dem. Rep. Congo | 20863.048 |
| Indonesia | 13369.005 |
| North Korea | 11553.577 |
| Ghana | 8202.510 |
| Madagascar | 7978.496 |
| India | -28812.449 |
| Ethiopia | -22502.583 |
| China | -16752.815 |
| Philippines | -9655.750 |
| Zimbabwe | -8890.743 |
| Nepal | -5859.519 |
| Viet Nam | -4999.429 |
| Uganda | -4576.158 |
| Burkina Faso | -4337.049 |
| Niger | -3617.583 |
| Country | Value |
|---|---|
| Nigeria | 97.94014 |
| Marshall Islands | 88.17223 |
| Timor-Leste | 87.20306 |
| Papua New Guinea | 85.49242 |
| Libya | 85.13396 |
| North Korea | 84.44479 |
| Greenland | 77.69338 |
| Iceland | 76.08632 |
| Sudan | 66.45726 |
| Congo | 65.73305 |
| Azerbaijan | -100.00000 |
| Egypt | -86.46321 |
| Macedonia | -83.75589 |
| Saint Lucia | -80.58197 |
| Rwanda | -78.99093 |
| Syrian Arab Republic | -71.15123 |
| Burkina Faso | -67.09132 |
| Comoros | -67.08868 |
| Eritrea | -66.89072 |
| Honduras | -65.43858 |
Downloadable table with full data
| Country | Value |
|---|---|
| Nigeria | 52805.3129 |
| South Africa | 29593.6434 |
| Indonesia | 19480.6093 |
| Tanzania | 18779.4125 |
| Mozambique | 17870.0413 |
| India | 9962.0335 |
| Dem. Rep. Congo | 9497.6106 |
| Zambia | 7498.0137 |
| Angola | 5670.8262 |
| Ghana | 3740.3204 |
| Zimbabwe | -6432.0229 |
| Ethiopia | -4338.3535 |
| Uganda | -2717.5779 |
| Botswana | -1463.5821 |
| Namibia | -944.1877 |
| Viet Nam | -920.1390 |
| Rwanda | -748.9198 |
| Swaziland | -744.3264 |
| Burkina Faso | -738.9729 |
| Burundi | -640.2910 |
| Country | Value |
|---|---|
| Madagascar | 96.74147 |
| Bhutan | 93.36600 |
| Afghanistan | 93.35732 |
| Moldova Republic of | 88.93088 |
| Nigeria | 85.30343 |
| Cape Verde | 82.80465 |
| Papua New Guinea | 78.06040 |
| Uzbekistan | 77.99681 |
| Congo | 76.74764 |
| Mauritius | 73.54996 |
| Turkmenistan | -100.00000 |
| Chile | -82.69580 |
| Argentina | -73.74461 |
| Puerto Rico | -68.08163 |
| Japan | -66.53380 |
| North Korea | -58.28064 |
| Serbia | -57.62913 |
| Rwanda | -57.54725 |
| Burkina Faso | -57.53504 |
| Iran Islamic Republic of | -55.40047 |
Downloadable table with full data
| Country | Value |
|---|---|
| Nigeria | 163815.995 |
| Bangladesh | 49751.746 |
| Tanzania | 19492.905 |
| Dem. Rep. Congo | 16512.804 |
| North Korea | 13321.155 |
| Mozambique | 11039.108 |
| India | 10734.362 |
| Madagascar | 8024.573 |
| Afghanistan | 7421.693 |
| Indonesia | 6640.821 |
| Ethiopia | -18311.726 |
| China | -13512.763 |
| Philippines | -9788.048 |
| Nepal | -5666.740 |
| Zimbabwe | -4650.323 |
| Burkina Faso | -4098.008 |
| Niger | -3269.260 |
| Senegal | -2887.683 |
| Uganda | -2363.108 |
| Viet Nam | -2331.514 |
| Country | Value |
|---|---|
| Nigeria | 93.09254 |
| Marshall Islands | 86.33247 |
| Timor-Leste | 86.30530 |
| Libya | 86.10756 |
| North Korea | 84.53367 |
| Papua New Guinea | 84.33227 |
| Greenland | 74.17450 |
| Gabon | 73.73470 |
| Iceland | 73.35414 |
| Sudan | 66.26216 |
| Azerbaijan | -100.00000 |
| Macedonia | -79.75107 |
| Rwanda | -79.58858 |
| Honduras | -76.10209 |
| Bahamas | -72.93482 |
| Saint Lucia | -72.87303 |
| Egypt | -67.49221 |
| Zimbabwe | -64.95982 |
| Burkina Faso | -62.26468 |
| Kuwait | -61.81510 |
Downloadable table with full data
| Country | Value |
|---|---|
| Nigeria | 216621.308 |
| Bangladesh | 49863.225 |
| Tanzania | 38272.318 |
| South Africa | 29108.209 |
| Mozambique | 28909.149 |
| Indonesia | 26121.431 |
| Dem. Rep. Congo | 26010.415 |
| India | 20696.396 |
| North Korea | 13218.478 |
| Angola | 9910.123 |
| Ethiopia | -22650.079 |
| China | -13538.406 |
| Zimbabwe | -11082.346 |
| Philippines | -9435.607 |
| Nepal | -5476.979 |
| Uganda | -5080.686 |
| Burkina Faso | -4836.981 |
| Niger | -3757.964 |
| Viet Nam | -3251.653 |
| Senegal | -3147.204 |
| Country | Value |
|---|---|
| Nigeria | 98.95851 |
| Marshall Islands | 91.15784 |
| Timor-Leste | 90.95049 |
| Papua New Guinea | 89.67219 |
| Libya | 89.20694 |
| North Korea | 87.77826 |
| Greenland | 77.96434 |
| Iceland | 76.32096 |
| Sudan | 69.42114 |
| Laos | 68.21180 |
| Azerbaijan | -100.00000 |
| Macedonia | -84.39432 |
| Rwanda | -79.30289 |
| Saint Lucia | -72.67408 |
| Egypt | -68.95801 |
| Burkina Faso | -66.12854 |
| Honduras | -64.72053 |
| Eritrea | -64.18513 |
| Kuwait | -63.76456 |
| Comoros | -61.38086 |
Downloadable table with full data
# CDR
df$country[is.na(df$cdr_ihme)]
[1] "Antigua and Barbuda" "Turkmenistan" "Qatar"
[4] "Bahrain" "Comoros" "Virgin Islands U.S."
# CFR
df$country[is.na(df$case_fatality_rate_2015)]
[1] "Sudan" "Cambodia"
[3] "Malta" "Antigua and Barbuda"
[5] "Gambia" "Djibouti"
[7] "Turkmenistan" "Qatar"
[9] "Luxembourg" "Bosnia and Herzegovina"
[11] "Canada" "Grenada"
[13] "Japan" "Italy"
[15] "Bahrain" "American Samoa"
[17] "Switzerland" "Ethiopia"
[19] "Belize" "Greece"
[21] "France" "Saint Vincent and the Grenadines"
[23] "Comoros" "Saint Lucia"
[25] "Bermuda" "Virgin Islands U.S."
df$country[is.na(df$case_fatality_rate_2014)]
[1] "Sudan" "Cambodia"
[3] "Malta" "Antigua and Barbuda"
[5] "Gambia" "Djibouti"
[7] "Turkmenistan" "Qatar"
[9] "Luxembourg" "Bosnia and Herzegovina"
[11] "Canada" "Grenada"
[13] "Japan" "Italy"
[15] "Bahrain" "American Samoa"
[17] "Switzerland" "Ethiopia"
[19] "Belize" "Greece"
[21] "France" "Saint Vincent and the Grenadines"
[23] "Comoros" "Saint Lucia"
[25] "Bermuda" "Virgin Islands U.S."
# HIV
df$country[is.na(df$newrel_hivpos)]
[1] "Iceland" "Algeria"
[3] "Mauritania" "Austria"
[5] "Turkmenistan" "Qatar"
[7] "Poland" "Hungary"
[9] "Luxembourg" "United Kingdom"
[11] "Bosnia and Herzegovina" "Korea Republic of"
[13] "Germany" "Bulgaria"
[15] "Italy" "Bahrain"
[17] "American Samoa" "Spain"
[19] "Denmark" "Switzerland"
[21] "Greece" "Croatia"
[23] "France" "Sweden"
[25] "Cyprus" "Comoros"
[27] "Virgin Islands U.S."
Using stand_dif ( (a-b)/(a+b)/2 )
(using variable case_fatality_rate_2015_adjusted)
Alberto asked: can you look at the code and compare case_fatality_rate_2015_adjusted and case_fatality_rate_2015
They are identical except that the adjusted rate has 17 missings, whereas the non adjusted rate has 26 missings.
(using variable gb_cfr)
(using reported_mdr)
(using p_mdr_new)
original_stand_diffstand_dif_compare(x = df$original_stand_dif,
y = df$gb_c_cdr,
yl = 'CDR WHO')
stand_dif_compare(x = df$original_stand_dif,
y = df$cdr_ihme,
yl = 'CDR GBD')
stand_dif_compare(x = df$original_stand_dif,
y = df$p_hiv_of,
yl = 'HIV prevalence')
stand_dif_compare(x = df$original_stand_dif,
y = df$p_mdr_new,
yl = 'MDR prevalence')
Difference using t-test (for p-value)
Welch Two Sample t-test
data: df$stand_dif[df$prevsurvey == 0] and df$stand_dif[df$prevsurvey == 1]
t = -2.1643, df = 21.066, p-value = 0.04207
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.78430145 -0.01573195
sample estimates:
mean of x mean of y
-0.09090786 0.30910884
Difference using t-test (for p-value)
Welch Two Sample t-test
data: x$original_stand_dif[x$prevsurvey == 0] and x$original_stand_dif[x$prevsurvey == 1]
t = -1.1965, df = 22.444, p-value = 0.244
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-8.447386 2.261924
sample estimates:
mean of x mean of y
1.027875 4.120606